Several, automatic vascular map segmentation of retinal fundus images have been proposed in literature [1]}, [2]}, [3]}, [4]}, [5]}. Fraz et al. have done a comprehensive literature survey on retinal blood vessel segmentation in [6]}. The retinal blood vessel segmentation techniques are popularly classified as: (i) supervised and (ii) unsupervised techniques. In supervised category, \(k\) -NN-classifier [7]}, Artificial Neural Network (ANN) [8]}, trainable COSFIER filters [9]}, Support Vector Machine (SVM) [10]}, Extreme Learning Machine (ELM) [11]}, Deep Neural Network (DNN) [4]}, [13]}, etc. have been explored for blood vessel segmentation as identification problem. However, supervised algorithms rely on the robust feature extraction followed by classification. In many approaches line detector [10]}, Gabor filters [15]}, [8]}, and gray level co-occurrence matrix (GLCM) [17]} based methods have been explored for feature extraction purpose. A feature vector is computed for each pixel using these approaches and classifier classify the pixels as the vessel and non-vessel pixels. These approaches are time-consuming process due to training. On the contrary, in the unsupervised category, filtering methods [1]}, [3]}, [20]}, vasculature tracing methods [21]}, curvelet based [22]}, morphological operators [23]}, have been used. In these approaches, classification of the vessel or non-vessel pixels is performed without training process, i.e., training data do not contribute to finding the model parameter.
